コード例 #1
0
ファイル: test_moments.py プロジェクト: juliantaylor/pandas
    def test_rolling_min(self):
        self._check_moment_func(mom.rolling_min, np.min)

        a = np.array([1,2,3,4,5])
        b = mom.rolling_min(a, window=100, min_periods=1)
        assert_almost_equal(b, np.ones(len(a)))

        self.assertRaises(ValueError, mom.rolling_min, np.array([1,2,3]), window=3, min_periods=5)
コード例 #2
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    def test_rolling_min(self):
        self._check_moment_func(mom.rolling_min, np.min)

        a = np.array([1,2,3,4,5])
        b = mom.rolling_min(a, window=100, min_periods=1)
        assert_almost_equal(b, np.ones(len(a)))

        self.assertRaises(ValueError, mom.rolling_min, np.array([1,2,3]), window=3, min_periods=5)
コード例 #3
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ファイル: test_moments.py プロジェクト: dwiel/pandas
    def test_rolling_functions_window_non_shrinkage(self):
        # GH 7764
        s = Series(range(4))
        s_expected = Series(np.nan, index=s.index)
        df = DataFrame([[1, 5], [3, 2], [3, 9], [-1, 0]], columns=['A', 'B'])
        df_expected = DataFrame(np.nan, index=df.index, columns=df.columns)
        df_expected_panel = Panel(items=df.index,
                                  major_axis=df.columns,
                                  minor_axis=df.columns)

        functions = [
            lambda x: mom.rolling_cov(
                x, x, pairwise=False, window=10, min_periods=5),
            lambda x: mom.rolling_corr(
                x, x, pairwise=False, window=10, min_periods=5),
            lambda x: mom.rolling_max(x, window=10, min_periods=5),
            lambda x: mom.rolling_min(x, window=10, min_periods=5),
            lambda x: mom.rolling_sum(x, window=10, min_periods=5),
            lambda x: mom.rolling_mean(x, window=10, min_periods=5),
            lambda x: mom.rolling_std(x, window=10, min_periods=5),
            lambda x: mom.rolling_var(x, window=10, min_periods=5),
            lambda x: mom.rolling_skew(x, window=10, min_periods=5),
            lambda x: mom.rolling_kurt(x, window=10, min_periods=5),
            lambda x: mom.rolling_quantile(
                x, quantile=0.5, window=10, min_periods=5),
            lambda x: mom.rolling_median(x, window=10, min_periods=5),
            lambda x: mom.rolling_apply(x, func=sum, window=10, min_periods=5),
            lambda x: mom.rolling_window(
                x, win_type='boxcar', window=10, min_periods=5),
        ]
        for f in functions:
            try:
                s_result = f(s)
                assert_series_equal(s_result, s_expected)

                df_result = f(df)
                assert_frame_equal(df_result, df_expected)
            except (ImportError):

                # scipy needed for rolling_window
                continue

        functions = [
            lambda x: mom.rolling_cov(
                x, x, pairwise=True, window=10, min_periods=5),
            lambda x: mom.rolling_corr(
                x, x, pairwise=True, window=10, min_periods=5),
            # rolling_corr_pairwise is depracated, so the following line should be deleted
            # when rolling_corr_pairwise is removed.
            lambda x: mom.rolling_corr_pairwise(x, x, window=10, min_periods=5
                                                ),
        ]
        for f in functions:
            df_result_panel = f(df)
            assert_panel_equal(df_result_panel, df_expected_panel)
コード例 #4
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ファイル: test_moments.py プロジェクト: JohnFNovak/pandas
    def test_rolling_min_how_resample(self):

        indices = [datetime(1975, 1, i) for i in range(1, 6)]
        # So that we can have 3 datapoints on last day (4, 10, and 20)
        indices.append(datetime(1975, 1, 5, 1))
        indices.append(datetime(1975, 1, 5, 2))
        series = Series(list(range(0, 5)) + [10, 20], index=indices)
        # Use floats instead of ints as values
        series = series.map(lambda x: float(x))
        # Sort chronologically
        series = series.sort_index()

        # Default how should be min
        expected = Series([0.0, 1.0, 2.0, 3.0, 4.0], index=[datetime(1975, 1, i, 0) for i in range(1, 6)])
        x = mom.rolling_min(series, window=1, freq="D")
        assert_series_equal(expected, x)
コード例 #5
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    def test_rolling_min_how_resample(self):

        indices = [datetime(1975, 1, i) for i in range(1, 6)]
        # So that we can have 3 datapoints on last day (4, 10, and 20)
        indices.append(datetime(1975, 1, 5, 1))
        indices.append(datetime(1975, 1, 5, 2))
        series = Series(list(range(0, 5)) + [10, 20], index=indices)
        # Use floats instead of ints as values
        series = series.map(lambda x: float(x))
        # Sort chronologically
        series = series.sort_index()

        # Default how should be min
        expected = Series([0.0, 1.0, 2.0, 3.0, 4.0],
                          index=[datetime(1975, 1, i, 0) for i in range(1, 6)])
        x = mom.rolling_min(series, window=1, freq='D')
        assert_series_equal(expected, x)
コード例 #6
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ファイル: test_moments.py プロジェクト: dwiel/pandas
    def test_rolling_functions_window_non_shrinkage(self):
        # GH 7764
        s = Series(range(4))
        s_expected = Series(np.nan, index=s.index)
        df = DataFrame([[1,5], [3, 2], [3,9], [-1,0]], columns=['A','B'])
        df_expected = DataFrame(np.nan, index=df.index, columns=df.columns)
        df_expected_panel = Panel(items=df.index, major_axis=df.columns, minor_axis=df.columns)

        functions = [lambda x: mom.rolling_cov(x, x, pairwise=False, window=10, min_periods=5),
                     lambda x: mom.rolling_corr(x, x, pairwise=False, window=10, min_periods=5),
                     lambda x: mom.rolling_max(x, window=10, min_periods=5),
                     lambda x: mom.rolling_min(x, window=10, min_periods=5),
                     lambda x: mom.rolling_sum(x, window=10, min_periods=5),
                     lambda x: mom.rolling_mean(x, window=10, min_periods=5),
                     lambda x: mom.rolling_std(x, window=10, min_periods=5),
                     lambda x: mom.rolling_var(x, window=10, min_periods=5),
                     lambda x: mom.rolling_skew(x, window=10, min_periods=5),
                     lambda x: mom.rolling_kurt(x, window=10, min_periods=5),
                     lambda x: mom.rolling_quantile(x, quantile=0.5, window=10, min_periods=5),
                     lambda x: mom.rolling_median(x, window=10, min_periods=5),
                     lambda x: mom.rolling_apply(x, func=sum, window=10, min_periods=5),
                     lambda x: mom.rolling_window(x, win_type='boxcar', window=10, min_periods=5),
                    ]
        for f in functions:
            try:
                s_result = f(s)
                assert_series_equal(s_result, s_expected)

                df_result = f(df)
                assert_frame_equal(df_result, df_expected)
            except (ImportError):

                # scipy needed for rolling_window
                continue

        functions = [lambda x: mom.rolling_cov(x, x, pairwise=True, window=10, min_periods=5),
                     lambda x: mom.rolling_corr(x, x, pairwise=True, window=10, min_periods=5),
                     # rolling_corr_pairwise is depracated, so the following line should be deleted
                     # when rolling_corr_pairwise is removed.
                     lambda x: mom.rolling_corr_pairwise(x, x, window=10, min_periods=5),
                    ]
        for f in functions:
            df_result_panel = f(df)
            assert_panel_equal(df_result_panel, df_expected_panel)
コード例 #7
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ファイル: plot_smac_runs.py プロジェクト: vliviu/autoweka
def aggregated_line_seeds(results, title):
    plt.close()
    sorted_points = np.array(sorted(results, key=itemgetter(1)))
    sorted_time = sorted_points[:, 1] / 60 / 60
    sorted_errors = sorted_points[:, 2]
    if is_regression:
        sorted_errors = np.log10(sorted_errors)

    y_mean = stats.rolling_mean(sorted_errors, 5)
    # y_std = stats.rolling_std(sorted_errors, 5)
    y_upper = stats.rolling_max(sorted_errors, 5)
    y_lower = stats.rolling_min(sorted_errors, 5)

    plt.plot(sorted_time, y_mean, color="red", label="Rolling mean")

    # plt.legend()
    plt.fill_between(sorted_time,
                     y_mean,
                     y_upper,
                     facecolor='gray',
                     interpolate=True,
                     alpha=0.5)
    plt.fill_between(sorted_time,
                     y_lower,
                     y_mean,
                     facecolor='gray',
                     interpolate=True,
                     alpha=0.5)

    plt.xlabel("Time (h)")
    if is_regression:
        plt.ylabel("log(RMSE)")
    else:
        plt.ylabel("% class. error")
        plt.ylim(0, 100)

    plt.margins(0.05, 0.05)

    plt.title(title)
    plt.savefig("%s/plots%s/trajectories-%s.aggregated.png" %
                (os.environ['AUTOWEKA_PATH'], suffix, title),
                bbox_inches='tight')
コード例 #8
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def llv(s, n):
    return moments.rolling_min(s, n)
コード例 #9
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# ax.set_yscale('log')
# ax.set_xlim(0,30)
# colors = sns.color_palette("husl", 25)
# for i in range(0,25):
# ax.scatter(time_by_seed[i], error_by_seed[i], c=cm.hsv(i/25.,1), s=[30]*len(time_by_seed[i]))
# ax.scatter(time_by_seed[i], error_by_seed[i], c=[colors[i]]*len(time_by_seed[i]), s=[30]*len(time_by_seed[i]))

ax1.set_xlabel('Time (h)')
ax1.set_ylabel('RMSE')
ax1.set_xlim(-1, 30)
y_mean = stats.rolling_mean(sorted_errors, 5)
y_std = stats.rolling_std(sorted_errors, 5)
# y_upper = y_mean + 2*y_std
y_upper = stats.rolling_max(sorted_errors, 5)
# y_lower = y_mean - 2*y_std
y_lower = stats.rolling_min(sorted_errors, 5)
sorted_data = DataFrame(data=sorted_points, columns=['time', 'binned_time', 'error', 'seed'])
# sns.jointplot("binned_time", "error", sorted_data)
# ax1.scatter(sorted_binned_time, sorted_errors)
ax1.plot(sorted_time, y_mean, color="red", label="Rolling mean")
# ax1.errorbar(sorted_binned_time, sorted_errors, marker='o', ms=8, yerr=3*y_std, ls='dotted', label="Rolling mean")
ax1.legend()
ax1.fill_between(sorted_time, y_mean, y_upper, facecolor='gray', interpolate=True, alpha=0.5)
ax1.fill_between(sorted_time, y_lower, y_mean, facecolor='gray', interpolate=True, alpha=0.5)
if not os.path.isdir("plots"):
    os.mkdir("plots")
#fig.savefig("plots/points.png", bbox_inches='tight')
fig.savefig("%s/plots%s/points-%s.png" % (os.environ['AUTOWEKA_PATH'], suffix, title), bbox_inches='tight')
# plt.show()

コード例 #10
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ファイル: extract_points.py プロジェクト: vliviu/autoweka
# ax.set_yscale('log')
# ax.set_xlim(0,30)
# colors = sns.color_palette("husl", 25)
# for i in range(0,25):
# ax.scatter(time_by_seed[i], error_by_seed[i], c=cm.hsv(i/25.,1), s=[30]*len(time_by_seed[i]))
# ax.scatter(time_by_seed[i], error_by_seed[i], c=[colors[i]]*len(time_by_seed[i]), s=[30]*len(time_by_seed[i]))

ax1.set_xlabel('Time (h)')
ax1.set_ylabel('RMSE')
ax1.set_xlim(-1, 30)
y_mean = stats.rolling_mean(sorted_errors, 5)
y_std = stats.rolling_std(sorted_errors, 5)
# y_upper = y_mean + 2*y_std
y_upper = stats.rolling_max(sorted_errors, 5)
# y_lower = y_mean - 2*y_std
y_lower = stats.rolling_min(sorted_errors, 5)
sorted_data = DataFrame(data=sorted_points,
                        columns=['time', 'binned_time', 'error', 'seed'])
# sns.jointplot("binned_time", "error", sorted_data)
# ax1.scatter(sorted_binned_time, sorted_errors)
ax1.plot(sorted_time, y_mean, color="red", label="Rolling mean")
# ax1.errorbar(sorted_binned_time, sorted_errors, marker='o', ms=8, yerr=3*y_std, ls='dotted', label="Rolling mean")
ax1.legend()
ax1.fill_between(sorted_time,
                 y_mean,
                 y_upper,
                 facecolor='gray',
                 interpolate=True,
                 alpha=0.5)
ax1.fill_between(sorted_time,
                 y_lower,
コード例 #11
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ファイル: ta.py プロジェクト: FreddieWitherden/ta
def llv(s, n):
    return moments.rolling_min(s, n)